Overview

Dataset statistics

Number of variables16
Number of observations10127
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory128.0 B

Variable types

Numeric8
Categorical6
Text2

Alerts

tipo_cartao is highly imbalanced (79.2%)Imbalance
id has unique valuesUnique
dependentes has 904 (8.9%) zerosZeros
iteracoes_12m has 399 (3.9%) zerosZeros

Reproduction

Analysis started2024-04-29 18:36:33.129042
Analysis finished2024-04-29 18:36:41.004056
Duration7.88 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct10127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3917761 × 108
Minimum7.0808208 × 108
Maximum8.2834308 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-04-29T15:36:41.093598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum7.0808208 × 108
5-th percentile7.0912039 × 108
Q17.1303677 × 108
median7.1792636 × 108
Q37.7314353 × 108
95-th percentile8.1421203 × 108
Maximum8.2834308 × 108
Range1.20261 × 108
Interquartile range (IQR)60106762

Descriptive statistics

Standard deviation36903783
Coefficient of variation (CV)0.049925462
Kurtosis-0.6156397
Mean7.3917761 × 108
Median Absolute Deviation (MAD)6347700
Skewness0.99560101
Sum7.4856516 × 1012
Variance1.3618892 × 1015
MonotonicityNot monotonic
2024-04-29T15:36:41.238769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
768805383 1
 
< 0.1%
711784908 1
 
< 0.1%
720133908 1
 
< 0.1%
803197833 1
 
< 0.1%
812222208 1
 
< 0.1%
757634583 1
 
< 0.1%
719362458 1
 
< 0.1%
789331908 1
 
< 0.1%
715616358 1
 
< 0.1%
806900508 1
 
< 0.1%
Other values (10117) 10117
99.9%
ValueCountFrequency (%)
708082083 1
< 0.1%
708083283 1
< 0.1%
708084558 1
< 0.1%
708085458 1
< 0.1%
708086958 1
< 0.1%
708095133 1
< 0.1%
708098133 1
< 0.1%
708099183 1
< 0.1%
708100533 1
< 0.1%
708103608 1
< 0.1%
ValueCountFrequency (%)
828343083 1
< 0.1%
828298908 1
< 0.1%
828294933 1
< 0.1%
828291858 1
< 0.1%
828288333 1
< 0.1%
828285858 1
< 0.1%
828281733 1
< 0.1%
828236133 1
< 0.1%
828227433 1
< 0.1%
828215508 1
< 0.1%

default
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
0
8500 
1
1627 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8500
83.9%
1 1627
 
16.1%

Length

2024-04-29T15:36:41.363788image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T15:36:41.568969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8500
83.9%
1 1627
 
16.1%

Most occurring characters

ValueCountFrequency (%)
0 8500
83.9%
1 1627
 
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8500
83.9%
1 1627
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8500
83.9%
1 1627
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8500
83.9%
1 1627
 
16.1%

idade
Real number (ℝ)

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.32596
Minimum26
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-04-29T15:36:41.681640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum73
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.016814
Coefficient of variation (CV)0.1730523
Kurtosis-0.28861992
Mean46.32596
Median Absolute Deviation (MAD)6
Skewness-0.033605016
Sum469143
Variance64.269307
MonotonicityNot monotonic
2024-04-29T15:36:41.816281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
44 500
 
4.9%
49 495
 
4.9%
46 490
 
4.8%
45 486
 
4.8%
47 479
 
4.7%
43 473
 
4.7%
48 472
 
4.7%
50 452
 
4.5%
42 426
 
4.2%
51 398
 
3.9%
Other values (35) 5456
53.9%
ValueCountFrequency (%)
26 78
0.8%
27 32
 
0.3%
28 29
 
0.3%
29 56
 
0.6%
30 70
 
0.7%
31 91
0.9%
32 106
1.0%
33 127
1.3%
34 146
1.4%
35 184
1.8%
ValueCountFrequency (%)
73 1
 
< 0.1%
70 1
 
< 0.1%
68 2
 
< 0.1%
67 4
 
< 0.1%
66 2
 
< 0.1%
65 101
1.0%
64 43
0.4%
63 65
0.6%
62 93
0.9%
61 93
0.9%

sexo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
F
5358 
M
4769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10127
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Length

2024-04-29T15:36:41.932970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T15:36:42.021757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
f 5358
52.9%
m 4769
47.1%

Most occurring characters

ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 5358
52.9%
M 4769
47.1%

dependentes
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3462032
Minimum0
Maximum5
Zeros904
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-04-29T15:36:42.108524image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2989083
Coefficient of variation (CV)0.55362142
Kurtosis-0.68301665
Mean2.3462032
Median Absolute Deviation (MAD)1
Skewness-0.020825536
Sum23760
Variance1.6871629
MonotonicityNot monotonic
2024-04-29T15:36:42.227755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
4 1574
15.5%
0 904
 
8.9%
5 424
 
4.2%
ValueCountFrequency (%)
0 904
 
8.9%
1 1838
18.1%
2 2655
26.2%
3 2732
27.0%
4 1574
15.5%
5 424
 
4.2%
ValueCountFrequency (%)
5 424
 
4.2%
4 1574
15.5%
3 2732
27.0%
2 2655
26.2%
1 1838
18.1%
0 904
 
8.9%

escolaridade
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
mestrado
3128 
ensino medio
2013 
na
1519 
sem educacao formal
1487 
graduacao
1013 

Length

Max length19
Median length12
Mean length9.7058359
Min length2

Characters and Unicode

Total characters98291
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowensino medio
2nd rowmestrado
3rd rowmestrado
4th rowensino medio
5th rowsem educacao formal

Common Values

ValueCountFrequency (%)
mestrado 3128
30.9%
ensino medio 2013
19.9%
na 1519
15.0%
sem educacao formal 1487
14.7%
graduacao 1013
 
10.0%
doutorado 967
 
9.5%

Length

2024-04-29T15:36:42.347404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T15:36:42.479048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
mestrado 3128
20.7%
ensino 2013
13.3%
medio 2013
13.3%
na 1519
10.1%
sem 1487
9.8%
educacao 1487
9.8%
formal 1487
9.8%
graduacao 1013
 
6.7%
doutorado 967
 
6.4%

Most occurring characters

ValueCountFrequency (%)
o 14042
14.3%
a 13114
13.3%
e 10128
10.3%
d 9575
9.7%
m 8115
8.3%
s 6628
6.7%
r 6595
6.7%
n 5545
 
5.6%
4987
 
5.1%
t 4095
 
4.2%
Other values (6) 15467
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 98291
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 14042
14.3%
a 13114
13.3%
e 10128
10.3%
d 9575
9.7%
m 8115
8.3%
s 6628
6.7%
r 6595
6.7%
n 5545
 
5.6%
4987
 
5.1%
t 4095
 
4.2%
Other values (6) 15467
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 98291
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 14042
14.3%
a 13114
13.3%
e 10128
10.3%
d 9575
9.7%
m 8115
8.3%
s 6628
6.7%
r 6595
6.7%
n 5545
 
5.6%
4987
 
5.1%
t 4095
 
4.2%
Other values (6) 15467
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 98291
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 14042
14.3%
a 13114
13.3%
e 10128
10.3%
d 9575
9.7%
m 8115
8.3%
s 6628
6.7%
r 6595
6.7%
n 5545
 
5.6%
4987
 
5.1%
t 4095
 
4.2%
Other values (6) 15467
15.7%

estado_civil
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
casado
4687 
solteiro
3943 
na
749 
divorciado
748 

Length

Max length10
Median length8
Mean length6.7783154
Min length2

Characters and Unicode

Total characters68644
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcasado
2nd rowsolteiro
3rd rowcasado
4th rowna
5th rowcasado

Common Values

ValueCountFrequency (%)
casado 4687
46.3%
solteiro 3943
38.9%
na 749
 
7.4%
divorciado 748
 
7.4%

Length

2024-04-29T15:36:42.630508image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T15:36:42.740133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
casado 4687
46.3%
solteiro 3943
38.9%
na 749
 
7.4%
divorciado 748
 
7.4%

Most occurring characters

ValueCountFrequency (%)
o 14069
20.5%
a 10871
15.8%
s 8630
12.6%
d 6183
9.0%
i 5439
 
7.9%
c 5435
 
7.9%
r 4691
 
6.8%
l 3943
 
5.7%
t 3943
 
5.7%
e 3943
 
5.7%
Other values (2) 1497
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 14069
20.5%
a 10871
15.8%
s 8630
12.6%
d 6183
9.0%
i 5439
 
7.9%
c 5435
 
7.9%
r 4691
 
6.8%
l 3943
 
5.7%
t 3943
 
5.7%
e 3943
 
5.7%
Other values (2) 1497
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 14069
20.5%
a 10871
15.8%
s 8630
12.6%
d 6183
9.0%
i 5439
 
7.9%
c 5435
 
7.9%
r 4691
 
6.8%
l 3943
 
5.7%
t 3943
 
5.7%
e 3943
 
5.7%
Other values (2) 1497
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 14069
20.5%
a 10871
15.8%
s 8630
12.6%
d 6183
9.0%
i 5439
 
7.9%
c 5435
 
7.9%
r 4691
 
6.8%
l 3943
 
5.7%
t 3943
 
5.7%
e 3943
 
5.7%
Other values (2) 1497
 
2.2%

salario_anual
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
menos que $40K
3561 
$40K - $60K
1790 
$80K - $120K
1535 
$60K - $80K
1402 
na
1112 

Length

Max length14
Median length12
Mean length10.931075
Min length2

Characters and Unicode

Total characters110699
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$60K - $80K
2nd rowmenos que $40K
3rd row$80K - $120K
4th rowmenos que $40K
5th row$60K - $80K

Common Values

ValueCountFrequency (%)
menos que $40K 3561
35.2%
$40K - $60K 1790
17.7%
$80K - $120K 1535
15.2%
$60K - $80K 1402
 
13.8%
na 1112
 
11.0%
$120K + 727
 
7.2%

Length

2024-04-29T15:36:42.858061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T15:36:43.311835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
5454
19.9%
40k 5351
19.5%
menos 3561
13.0%
que 3561
13.0%
60k 3192
11.6%
80k 2937
10.7%
120k 2262
8.2%
na 1112
 
4.1%

Most occurring characters

ValueCountFrequency (%)
17303
15.6%
$ 13742
12.4%
K 13742
12.4%
0 13742
12.4%
e 7122
 
6.4%
4 5351
 
4.8%
- 4727
 
4.3%
n 4673
 
4.2%
m 3561
 
3.2%
u 3561
 
3.2%
Other values (9) 23175
20.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110699
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17303
15.6%
$ 13742
12.4%
K 13742
12.4%
0 13742
12.4%
e 7122
 
6.4%
4 5351
 
4.8%
- 4727
 
4.3%
n 4673
 
4.2%
m 3561
 
3.2%
u 3561
 
3.2%
Other values (9) 23175
20.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110699
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17303
15.6%
$ 13742
12.4%
K 13742
12.4%
0 13742
12.4%
e 7122
 
6.4%
4 5351
 
4.8%
- 4727
 
4.3%
n 4673
 
4.2%
m 3561
 
3.2%
u 3561
 
3.2%
Other values (9) 23175
20.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110699
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17303
15.6%
$ 13742
12.4%
K 13742
12.4%
0 13742
12.4%
e 7122
 
6.4%
4 5351
 
4.8%
- 4727
 
4.3%
n 4673
 
4.2%
m 3561
 
3.2%
u 3561
 
3.2%
Other values (9) 23175
20.9%

tipo_cartao
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
blue
9436 
silver
 
555
gold
 
116
platinum
 
20

Length

Max length8
Median length4
Mean length4.1175077
Min length4

Characters and Unicode

Total characters41698
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblue
2nd rowblue
3rd rowblue
4th rowblue
5th rowblue

Common Values

ValueCountFrequency (%)
blue 9436
93.2%
silver 555
 
5.5%
gold 116
 
1.1%
platinum 20
 
0.2%

Length

2024-04-29T15:36:43.469353image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T15:36:43.579673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
blue 9436
93.2%
silver 555
 
5.5%
gold 116
 
1.1%
platinum 20
 
0.2%

Most occurring characters

ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
b 9436
22.6%
i 575
 
1.4%
s 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
g 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
b 9436
22.6%
i 575
 
1.4%
s 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
g 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
b 9436
22.6%
i 575
 
1.4%
s 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
g 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 10127
24.3%
e 9991
24.0%
u 9456
22.7%
b 9436
22.6%
i 575
 
1.4%
s 555
 
1.3%
v 555
 
1.3%
r 555
 
1.3%
g 116
 
0.3%
o 116
 
0.3%
Other values (6) 216
 
0.5%

meses_de_relacionamento
Real number (ℝ)

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.928409
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-04-29T15:36:43.693313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.9864163
Coefficient of variation (CV)0.22228695
Kurtosis0.40010012
Mean35.928409
Median Absolute Deviation (MAD)4
Skewness-0.10656536
Sum363847
Variance63.782846
MonotonicityNot monotonic
2024-04-29T15:36:43.820910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
36 2463
24.3%
37 358
 
3.5%
34 353
 
3.5%
38 347
 
3.4%
39 341
 
3.4%
40 333
 
3.3%
31 318
 
3.1%
35 317
 
3.1%
33 305
 
3.0%
30 300
 
3.0%
Other values (34) 4692
46.3%
ValueCountFrequency (%)
13 70
0.7%
14 16
 
0.2%
15 34
 
0.3%
16 29
 
0.3%
17 39
 
0.4%
18 58
0.6%
19 63
0.6%
20 74
0.7%
21 83
0.8%
22 105
1.0%
ValueCountFrequency (%)
56 103
1.0%
55 42
 
0.4%
54 53
 
0.5%
53 78
0.8%
52 62
 
0.6%
51 80
0.8%
50 96
0.9%
49 141
1.4%
48 162
1.6%
47 171
1.7%

qtd_produtos
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8125802
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-04-29T15:36:43.923226image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5544079
Coefficient of variation (CV)0.40770496
Kurtosis-1.0061305
Mean3.8125802
Median Absolute Deviation (MAD)1
Skewness-0.16245241
Sum38610
Variance2.4161838
MonotonicityNot monotonic
2024-04-29T15:36:44.024382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
2 1243
12.3%
1 910
 
9.0%
ValueCountFrequency (%)
1 910
 
9.0%
2 1243
12.3%
3 2305
22.8%
4 1912
18.9%
5 1891
18.7%
6 1866
18.4%
ValueCountFrequency (%)
6 1866
18.4%
5 1891
18.7%
4 1912
18.9%
3 2305
22.8%
2 1243
12.3%
1 910
 
9.0%

iteracoes_12m
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4553175
Minimum0
Maximum6
Zeros399
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-04-29T15:36:44.116750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1062251
Coefficient of variation (CV)0.45054261
Kurtosis0.00086265663
Mean2.4553175
Median Absolute Deviation (MAD)1
Skewness0.011005626
Sum24865
Variance1.2237341
MonotonicityNot monotonic
2024-04-29T15:36:44.219247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
4 1392
13.7%
0 399
 
3.9%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
0 399
 
3.9%
1 1499
14.8%
2 3227
31.9%
3 3380
33.4%
4 1392
13.7%
5 176
 
1.7%
6 54
 
0.5%
ValueCountFrequency (%)
6 54
 
0.5%
5 176
 
1.7%
4 1392
13.7%
3 3380
33.4%
2 3227
31.9%
1 1499
14.8%
0 399
 
3.9%

meses_inativo_12m
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3411672
Minimum0
Maximum6
Zeros29
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-04-29T15:36:44.319568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0106224
Coefficient of variation (CV)0.4316746
Kurtosis1.0985226
Mean2.3411672
Median Absolute Deviation (MAD)1
Skewness0.63306113
Sum23709
Variance1.0213576
MonotonicityNot monotonic
2024-04-29T15:36:44.416194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
0 29
 
0.3%
ValueCountFrequency (%)
0 29
 
0.3%
1 2233
22.0%
2 3282
32.4%
3 3846
38.0%
4 435
 
4.3%
5 178
 
1.8%
6 124
 
1.2%
ValueCountFrequency (%)
6 124
 
1.2%
5 178
 
1.8%
4 435
 
4.3%
3 3846
38.0%
2 3282
32.4%
1 2233
22.0%
0 29
 
0.3%
Distinct9272
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
2024-04-29T15:36:44.669915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.2719463
Min length8

Characters and Unicode

Total characters83770
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9042 ?
Unique (%)89.3%

Sample

1st row12.691,51
2nd row8.256,96
3rd row3.418,56
4th row3.313,03
5th row4.716,22
ValueCountFrequency (%)
1.438,21 11
 
0.1%
34.516,45 10
 
0.1%
1.438,72 10
 
0.1%
1.438,31 10
 
0.1%
34.516,25 10
 
0.1%
34.516,56 10
 
0.1%
1.438,92 10
 
0.1%
1.438,61 10
 
0.1%
1.438,33 10
 
0.1%
34.516,07 10
 
0.1%
Other values (9262) 10026
99.0%
2024-04-29T15:36:45.027296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 10127
12.1%
, 10127
12.1%
1 8426
10.1%
2 7711
9.2%
3 7322
8.7%
4 6715
8.0%
5 6046
7.2%
6 6038
7.2%
8 5698
6.8%
7 5240
6.3%
Other values (2) 10320
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83770
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 10127
12.1%
, 10127
12.1%
1 8426
10.1%
2 7711
9.2%
3 7322
8.7%
4 6715
8.0%
5 6046
7.2%
6 6038
7.2%
8 5698
6.8%
7 5240
6.3%
Other values (2) 10320
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83770
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 10127
12.1%
, 10127
12.1%
1 8426
10.1%
2 7711
9.2%
3 7322
8.7%
4 6715
8.0%
5 6046
7.2%
6 6038
7.2%
8 5698
6.8%
7 5240
6.3%
Other values (2) 10320
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83770
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 10127
12.1%
, 10127
12.1%
1 8426
10.1%
2 7711
9.2%
3 7322
8.7%
4 6715
8.0%
5 6046
7.2%
6 6038
7.2%
8 5698
6.8%
7 5240
6.3%
Other values (2) 10320
12.3%
Distinct10035
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
2024-04-29T15:36:45.379595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.043152
Min length6

Characters and Unicode

Total characters81453
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9945 ?
Unique (%)98.2%

Sample

1st row1.144,90
2nd row1.291,45
3rd row1.887,72
4th row1.171,56
5th row816,08
ValueCountFrequency (%)
3.851,51 3
 
< 0.1%
1.388,72 3
 
< 0.1%
7.855,26 2
 
< 0.1%
4.100,15 2
 
< 0.1%
4.160,20 2
 
< 0.1%
4.651,47 2
 
< 0.1%
2.237,14 2
 
< 0.1%
4.442,32 2
 
< 0.1%
1.836,23 2
 
< 0.1%
4.424,76 2
 
< 0.1%
Other values (10025) 10105
99.8%
2024-04-29T15:36:45.709101image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 10127
12.4%
. 9967
12.2%
4 8290
10.2%
1 7794
9.6%
2 6838
8.4%
3 6774
8.3%
5 5811
7.1%
7 5443
6.7%
8 5241
6.4%
6 5124
6.3%
Other values (2) 10044
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81453
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 10127
12.4%
. 9967
12.2%
4 8290
10.2%
1 7794
9.6%
2 6838
8.4%
3 6774
8.3%
5 5811
7.1%
7 5443
6.7%
8 5241
6.4%
6 5124
6.3%
Other values (2) 10044
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81453
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 10127
12.4%
. 9967
12.2%
4 8290
10.2%
1 7794
9.6%
2 6838
8.4%
3 6774
8.3%
5 5811
7.1%
7 5443
6.7%
8 5241
6.4%
6 5124
6.3%
Other values (2) 10044
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81453
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 10127
12.4%
. 9967
12.2%
4 8290
10.2%
1 7794
9.6%
2 6838
8.4%
3 6774
8.3%
5 5811
7.1%
7 5443
6.7%
8 5241
6.4%
6 5124
6.3%
Other values (2) 10044
12.3%

qtd_transacoes_12m
Real number (ℝ)

Distinct126
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.858695
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size79.2 KiB
2024-04-29T15:36:45.859962image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.47257
Coefficient of variation (CV)0.36190322
Kurtosis-0.36716324
Mean64.858695
Median Absolute Deviation (MAD)17
Skewness0.15367307
Sum656824
Variance550.96156
MonotonicityNot monotonic
2024-04-29T15:36:46.005161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 208
 
2.1%
71 203
 
2.0%
75 203
 
2.0%
69 202
 
2.0%
82 202
 
2.0%
76 198
 
2.0%
77 197
 
1.9%
70 193
 
1.9%
74 190
 
1.9%
78 190
 
1.9%
Other values (116) 8141
80.4%
ValueCountFrequency (%)
10 4
 
< 0.1%
11 2
 
< 0.1%
12 4
 
< 0.1%
13 5
 
< 0.1%
14 9
 
0.1%
15 16
0.2%
16 13
0.1%
17 13
0.1%
18 23
0.2%
19 11
0.1%
ValueCountFrequency (%)
139 1
 
< 0.1%
138 1
 
< 0.1%
134 1
 
< 0.1%
132 1
 
< 0.1%
131 6
0.1%
130 5
< 0.1%
129 6
0.1%
128 10
0.1%
127 12
0.1%
126 10
0.1%

Interactions

2024-04-29T15:36:39.551276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:34.406630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:35.209982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:35.918334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:36.627923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:37.324399image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:38.033412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:38.816735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:39.772923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:34.546256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:35.301263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:36.008400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:36.716411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:37.414158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:38.126991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:38.909463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:39.859696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:34.640005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:35.383607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:36.093045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:36.798580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:37.498700image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:38.213759image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:38.998230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:39.953445image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:34.733754image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:35.471212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:36.180229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:36.885939image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:37.587156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:38.302536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:39.088804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:40.039563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:34.822517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:35.551052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:36.264647image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:36.965316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:37.668868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:38.388617image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:39.172962image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:40.139166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:34.918262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:35.641811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:36.351876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:37.051625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:37.757034image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:38.478207image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:39.265713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:40.234279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:35.016000image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:35.733965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:36.444316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:37.141973image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:37.847836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:38.571741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:39.360241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:40.337279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:35.112741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:35.826718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:36.535156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:37.232264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:37.940660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:38.711824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-04-29T15:36:39.455009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-04-29T15:36:40.538764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-29T15:36:40.873652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddefaultidadesexodependentesescolaridadeestado_civilsalario_anualtipo_cartaomeses_de_relacionamentoqtd_produtositeracoes_12mmeses_inativo_12mlimite_creditovalor_transacoes_12mqtd_transacoes_12m
0768805383045M3ensino mediocasado$60K - $80Kblue3953112.691,511.144,9042
1818770008049F5mestradosolteiromenos que $40Kblue446218.256,961.291,4533
2713982108051M3mestradocasado$80K - $120Kblue364013.418,561.887,7220
3769911858040F4ensino medionamenos que $40Kblue343143.313,031.171,5620
4709106358040M3sem educacao formalcasado$60K - $80Kblue215014.716,22816,0828
5713061558044M2mestradocasado$40K - $60Kblue363214.010,691.088,0724
6810347208051M4nacasado$120K +gold4663134.516,721.330,8731
7818906208032M0ensino mediona$60K - $80Ksilver2722229.081,491.538,3236
8710930508037M3sem educacao formalsolteiro$60K - $80Kblue3650222.352,501.350,1424
9719661558048M2mestradosolteiro$80K - $120Kblue3663311.656,411.441,7332
iddefaultidadesexodependentesescolaridadeestado_civilsalario_anualtipo_cartaomeses_de_relacionamentoqtd_produtositeracoes_12mmeses_inativo_12mlimite_creditovalor_transacoes_12mqtd_transacoes_12m
10117712503408057M2mestradocasado$80K - $120Kblue4064317.925,3317.498,70111
10118713755458150M1nana$80K - $120Kblue366439.959,9610.310,4163
10119716893683155F3sem educacao formalsolteironablue4743314.657,856.009,1853
10120710841183054M1ensino mediosolteiro$60K - $80Kblue3450213.940,6215.577,67114
10121713899383056F1mestradosolteiromenos que $40Kblue504413.688,9514.596,49120
10122772366833050M2mestradosolteiro$40K - $60Kblue403324.003,9115.476,26117
10123710638233141M2nadivorciado$40K - $60Kblue254324.277,048.764,8869
10124716506083144F1ensino mediocasadomenos que $40Kblue365435.409,1610.291,7860
10125717406983130M2mestradona$40K - $60Kblue364335.281,848.395,6262
10126714337233143F2mestradocasadomenos que $40Ksilver2564210.388,8010.294,9661